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Dissertations / Theses on the topic 'Random effects'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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1

Putcha, Venkata Rama Prasad. "Random effects in survival analysis." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312431.

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2

Lee, Sungwook. "Semiparametric regression with random effects /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842547.

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3

Hunt, Colleen Helen. "Inference for general random effects models." Title page, table of contents and abstract only, 2003. http://web4.library.adelaide.edu.au/theses/09SM/09smh9394.pdf.

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"October 13, 2003" Bibliography: leaves 102-105. This work describes methods associated with general random effects models. Part one describes a technique for investigating mean-variance relationships in random effects models. Part two derives and approximation to the likelihood function using a Laplace expansion to the fourth order.
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4

Sanogo, Kakotan. "Tolerance Intervals in Random-Effects Models." VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/1661.

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In the pharmaceutical setting, it is often necessary to establish the shelf life of a drug product and sometimes suitable to assess the risk of product failure at the desired expiry period. The current statistical methodology use confidence intervals for the predicted mean to establish the expiry period and prediction intervals for a predicted new assay value or a tolerance interval for a proportion of the population for use in a risk assessment. A major concern is that most methodology treat a homogeneous subpopulation, say batch, either as a fixed effect and therefore uses a fixed-effects regression model (Graybill, 1976) or as a mixed-effects model limited to balanced data structures (Jonsson, 2003). However, batch is definitely a random effect as this fact has been reflected by some recent methodology [Altan, Cabrera and Shoung (2005), Hoffman and Kringle (2005)]. Thus, to assess the risk of product failure at expiry, it is necessary to use tolerance intervals since they provide an estimate of the proportion of assay values and/or batches failing at the expiry period. In this thesis, we illustrate the methodology described by Jonsson (2003) to construct β-expectation tolerance limits for longitudinal data in a random-effects setting. We underline the limitations of Jonsson’s approach to constructing tolerance intervals and highlight the need for a better methodology.
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Skoglund, Jimmy. "Essays on random effects models and GARCH." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (Ekonomiska forskningsinstitutet vid Handelshögsk.) (EFI), 2001. http://www.hhs.se/efi.summary/553.htm.

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6

Higgins, Julian P. T. "Exploiting information in random effects meta-analysis." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387704.

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7

Kerekes, Andrea. "Random effects on the solar f-mode." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444227.

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8

Zhu, Chang Qing. "Statistical methods for Weibull based random effects models." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/876/.

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9

Marques-da-Silva, Antonio Hermes. "Gradient test under non-parametric random effects models." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12645/.

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The gradient test proposed by Terrell (2002) is an alternative to the likelihood ratio, Wald and Rao tests. The gradient statistic is the result of the inner product of two vectors — the gradient of the likelihood under null hypothesis (hence the name) and the result of the difference between the estimate under alternative hypothesis and the estimate under null hypothesis. Therefore the gradient statistic is computationally less expensive than Wald and Rao statistics as it does not require matrix operations in its formula. Under some regularity conditions, the gradient statistic has χ2 distribution under null hypothesis. The generalised linear model (GLM) introduced by Nelder & Wedderburn (1972) is one of the most important classes of statistical models. It incorporates the classical regression modelling and analysis of variance either for continuous response and categorical response variables under the exponential family. The random effects model extends the standard GLM for situations where the model does not describe appropriately the variability in the data (overdispersion) (Aitkin, 1996a). We propose a new unified notation for GLM with random effects and the gradient statistic formula for testing fixed effects parameters on these models. We also develop the Fisher information formulae used to obtain the Rao and Wald statistics. Our main interest in this thesis is to investigate the finite sample performance of the gradient test on generalised linear models with random effects. For this we propose and extensive simulation experiment to study the type I error and the local power of the gradient test using the methodology developed by Peers (1971) and Hayakawa (1975). We also compare the local power of the test with the local power of the tests of the likelihood ratio, of Wald and Rao tests.
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10

Meddings, D. P. "Statistical inference in mixture models with random effects." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1455733/.

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There is currently no existing asymptotic theory for statistical inference on the maximum likelihood estimators of the parameters in a mixture of linear mixed models (MLMMs). Despite this many researchers assume the estimators are asymptotically normally distributed with covariance matrix given by the inverse of the information matrix. Mixture models create new identifability problems that are not inherited from the underlying linear mixed model (LMM), and this subject has not been investigated for these models. Since identifability is a prerequisite for the existence of a consistent estimator of the model parameters, then this is an important area of research that has been neglected. MLMMs are mixture models with random effects, and they are typically used in medical and genetics settings where random heterogeneity in repeated measures data are observed between measurement units (people, genes), but where it is assumed the units belong to one and only one of a finite number of sub-populations or components. This is expressed probabalistically by using a sub-population specific probability distribution function which are often called the component distribution functions. This thesis is motivated by the belief that the use of MLMMs in applied settings such as these is being held back by the lack of development of the statistical inference framework. Specifically this thesis has the following primary objectives; i To investigate the quality of statistical inference provided by different information matrix based methods of confidence interval construction. ii To investigate the impact of component distribution function separation on the quality of statistical inference, and to propose a new method to quantify this separation. iii To determine sufficient conditions for identifiability of MLMMs.
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11

Ciera, James Mbugua. "Approximate bayes random effects models for large datasets." Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3426923.

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Many medical studies collect functional data, such as trajectories in a biomarker over time. It is of interest to estimate the trajectories and identify or predict clinically-important features. Linear mixed effects (LME) models are commonly used in such cases, with non-linear effects easily incorporated through splines. However, for sufficient flexibility, it is often necessary to use adaptive splines in which the number and locations of knots is unknown and potentially varying across subjects. This can be accomplished with MCMC methodology, using reversible jump or stochastic search variable selection. However, such approaches are slow and infeasible to implement routinely, particularly for large data sets. Motivated by methods proposed in the machine learning literature for compressive sensing, we focus on relevant vector machine (RVM) methodology - a fast approximate Bayes functional data analysis approach that relies on sparseness-favouring hierarchical priors for basis coefficients. Recent literature on the use of RVM methodology is restricted to models that assume that the distribution of the basis coefficients is centered at zero with diagonal covariance. However, in many longitudinal and functional data analysis applications, centering at zero is an unrealistic assumption and does not allow shrinkage towards a population-averaged function. In this work, we develop a generalized multi-task relevant vector machine (MT-RVM) methodology that generates sparse functional linear mixed models to estimate both population-average and subject-specific curves. In particular, we first consider an LME model that assumes independent random effects and then extend the approach to a more generalized LME model with correlated random effects. Further, we extend the application of the generalized MT-RVM methodology into multi-level relevant vector machine (ML-RVM) methodology to generate a sparse multi-level functional mixed model. The analysis of basal body temperature curves over the menstrual cycle has been the motivating application for all the developed methods.
Molti studi medici raccolgono dati in forma funzionale come ad esempio le traiettorie in un bio-marcatore nel corso del tempo. Di questi dati di interesse stimare le traiettorie e individuare o predire caratteristiche clinicamente importanti. I modelli lineari ad effetti misti (LME) sono comunemente utilizzati in questi casi, anche utilizzando effetti non-lineari che si possono includere facilmente attraverso splines. Tuttavia, per ottenere una flessibilità adeguata, spesso necessario utilizzare splines adattive in cui il numero e la posizione dei nodi ignoto e potenzialmente variabile tra soggetti. In questo contesto si utilizzano strumenti di tipo MCMC (Markov Chain Monte Carlo), come ad esempio il reversible jump o la selezione di variabili attraverso ricerca stocastica. Questi approcci sono, tuttavia, lenti e difficilmente utilizzabili in contesti in cui si ripetono spesso le operazioni di stima, in particolare per grandi dati set. A partire dagli strumenti sviluppati nella letteratura del compressive sensing in ambito di machine learning, ci siamo concentrati sulle relevant vector machine (RVM) - un approccio di analisi di dati funzionali bayesiano che utilizza veloci approssimazioni che sfruttano distribuzioni a priori gerarchiche per i coefficienti delle basi che ne favoriscano la sparsit. La letteratura recente per l’uso della metodologia RVM limitata ai modelli che assumono che una distribuzione dei coefficienti base centrata sullo zero con matrice di varianze e covarianze diagonale. In molte applicazioni su dati longitudinali e funzionali, tuttavia, la centratura sullo zero risulta essere una ipotesi poco realistica non consentendo il restringimento ad una funzione centrata sulla media della popolazione. In questo lavoro, abbiamo sviluppato una "multi-task relevant vector machine" generalizzata (MT-RVM), che genera modelli funzionali lineari misti sparsi per stimare sia la curva della media della popolazione che la curva specifica per soggetto. In particolare, in primo luogo abbiamo considerato un modello LME che assume effetti casuali indipendenti e successivamente abbiamo esteso questo approccio ad un modello LME pi generalizzato con effetti casuali correlati. Inoltre, abbiamo esteso la metodologia MT-RVM generalizzata alla situazione in cui sono disponibili diversi livelli di gerarchia, ottenendo una “multi-level relevant vector machine” (ML-RVM) che genera un modello multi-level funzionale sparso ad effetti misti. I metodi sviluppati sono stati motivati dal problema di analizzare le curve della temperatura basale durante il ciclo mestruale, e tale applicazione viene considerata come esemplificazione durante tutta la tesi.
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12

Kidney, Darren. "Random coeffcient models for complex longitudinal data." Thesis, University of St Andrews, 2014. http://hdl.handle.net/10023/6386.

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Longitudinal data are common in biological research. However, real data sets vary considerably in terms of their structure and complexity and present many challenges for statistical modelling. This thesis proposes a series of methods using random coefficients for modelling two broad types of longitudinal response: normally distributed measurements and binary recapture data. Biased inference can occur in linear mixed-effects modelling if subjects are drawn from a number of unknown sub-populations, or if the residual covariance is poorly specified. To address some of the shortcomings of previous approaches in terms of model selection and flexibility, this thesis presents methods for: (i) determining the presence of latent grouping structures using a two-step approach, involving regression splines for modelling functional random effects and mixture modelling of the fitted random effects; and (ii) flexible of modelling of the residual covariance matrix using regression splines to specify smooth and potentially non-monotonic variance and correlation functions. Spatially explicit capture-recapture methods for estimating the density of animal populations have shown a rapid increase in popularity over recent years. However, further refinements to existing theory and fitting software are required to apply these methods in many situations. This thesis presents: (i) an analysis of recapture data from an acoustic survey of gibbons using supplementary data in the form of estimated angles to detections, (ii) the development of a multi-occasion likelihood including a model for stochastic availability using a partially observed random effect (interpreted in terms of calling behaviour in the case of gibbons), and (iii) an analysis of recapture data from a population of radio-tagged skates using a conditional likelihood that allows the density of animal activity centres to be modelled as functions of time, space and animal-level covariates.
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13

Devamitta, Perera Muditha Virangika. "Robustness of normal theory inference when random effects are not normally distributed." Kansas State University, 2011. http://hdl.handle.net/2097/8786.

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Master of Science
Department of Statistics
Paul I. Nelson
The variance of a response in a one-way random effects model can be expressed as the sum of the variability among and within treatment levels. Conventional methods of statistical analysis for these models are based on the assumption of normality of both sources of variation. Since this assumption is not always satisfied and can be difficult to check, it is important to explore the performance of normal based inference when normality does not hold. This report uses simulation to explore and assess the robustness of the F-test for the presence of an among treatment variance component and the normal theory confidence interval for the intra-class correlation coefficient under several non-normal distributions. It was found that the power function of the F-test is robust for moderately heavy-tailed random error distributions. But, for very heavy tailed random error distributions, power is relatively low, even for a large number of treatments. Coverage rates of the confidence interval for the intra-class correlation coefficient are far from nominal for very heavy tailed, non-normal random effect distributions.
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14

Lai, Xin. "Extensions on long-term survivor model with random effects /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ms-b3008233xf.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.
"Submitted to Department of Management Sciences in partial fulfillment of the requirement for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 118-126)
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15

Alkhamisi, Mahdi. "Asymptotic analysis of the one-way random effects models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ50063.pdf.

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16

Murphy, Dennis John. "Post-data pivotal inference in balanced random effects models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ51659.pdf.

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17

Wang, Yaqin. "Estimation of accelerated failure time models with random effects." [Ames, Iowa : Iowa State University], 2006.

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18

Al-Aboud, Fahad M. "Random effects modelling for prediction from incomplete longitudinal data." Thesis, Lancaster University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364322.

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19

Chan, Karen Pui-Shan. "Kernel density estimation, Bayesian inference and random effects model." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/13350.

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This thesis contains results of a study in kernel density estimation, Bayesian inference and random effects models, with application to forensic problems. Estimation of the Bayes' factor in a forensic science problem involved the derivation of predictive distributions in non-standard situations. The distribution of the values of a characteristic of interest among different items in forensic science problems is often non-Normal. Background, or training, data were available to assist in the estimation of the distribution for measurements on cat and dog hairs. An informative prior, based on the kernel method of density estimation, was used to derive the appropriate predictive distributions. The training data may be considered to be derived from a random effects model. This was taken into consideration in modelling the Bayes' factor. The usual assumption of the random factor being Normally distributed is unrealistic, so a kernel density estimate was used as the distribution of the unknown random factor. Two kernel methods were employed: the ordinary and adaptive kernel methods. The adaptive kernel method allowed for the longer tail, where little information was available. Formulae for the Bayes' factor in a forensic science context were derived assuming the training data were grouped or not grouped (for example, hairs from one cat would be thought of as belonging to the same group), and that the within-group variance was or was not known. The Bayes' factor, assuming known within-group variance, for the training data, grouped or not grouped, was extended to the multivariate case. The method was applied to a practical example in a bivariate situation. Similar modelling of the Bayes' factor was derived to cope with a particular form of mixture data. Boundary effects were also taken into consideration. Application of kernel density estimation to make inferences about the variance components under the random effects model was studied. Employing the maximum likelihood estimation method, it was shown that the between-group variance and the smoothing parameter in the kernel density estimation were related. They were not identifiable separately. With the smoothing parameter fixed at some predetermined value, the within-and between-group variance estimates from the proposed model were equivalent to the usual ANOVA estimates. Within the Bayesian framework, posterior distribution for the variance components, using various prior distributions for the parameters were derived incorporating kernel density functions. The modes of these posterior distributions were used as estimates for the variance components. A Student-t within a Bayesian framework was derived after introduction of a prior for the smoothing prameter. Two methods of obtaining hyper-parameters for the prior were suggested, both involving empirical Bayes methods. They were a modified leave-one-out maximum likelihood method and a method of moments based on the optimum smoothing parameter determined from Normality assumption.
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Carwell, James W. "PYROTECHNIC SHOCK AND RANDOM VIBRATION EFFECTS ON CRYSTAL OSCILLATORS." International Foundation for Telemetering, 2001. http://hdl.handle.net/10150/607695.

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International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada
Today’s telemetry specifications are requiring electronic systems to not only survive, but operate through severe dynamic environments. Pyrotechnic shock and Random Vibration are among these environments and have proven to be a challenge for systems that rely on highly stable, low phase noise signal sources. This paper will mathematically analyze how Pyrotechnic shock and Random Vibration events deteriorate the phase noise of crystal oscillators (XO).
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Häggström, Lundevaller Erling. "Tests of random effects in linear and non-linear models." Doctoral thesis, Umeå universitet, Statistik, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-15.

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Du, Ye Ting. "Simultaneous fixed and random effects selection in finite mixtures of linear mixed-effects models." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110592.

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Linear mixed-effects (LME) models are frequently used for modeling longitudinal data. One complicating factor in the analysis of such data is that samples are sometimes obtained from a population with significant underlying heterogeneity, which would be hard to capture by a single LME model. Such problems may be addressed by a finite mixture of linear mixed-effects (FMLME) models, which segments the population into subpopulations and models each subpopulation by a distinct LME model. Often in the initial stage of a study, a large number of predictors are introduced. However, their associations to the response variable vary from one component to another of the FMLME model. To enhance predictability and to obtain a parsimonious model, it is of great practical interest to identify the important effects, both fixed and random, in the model. Traditional variable selection techniques such as stepwise deletion and subset selection are computationally expensive even with modest numbers of covariates and components in the mixture model. In this thesis, we introduce a penalized likelihood approach and propose a nested EM algorithm for efficient numerical computations. The estimators are shown to possess consistency and sparsity properties and asymptotic normality. We illustrate the performance of the proposed method through simulations and a real data example.
Les modèles linéaires mixtes (LME) sont fréquemment employés pour la modélisation des données longitudinales. Un facteur qui complique l'analyse de ce genre de données est que les échantillons sont parfois obtenus à partir d'une population d'importante hétérogénéité sous-jacente, qui serait difficile à capter par un seul LME. De tels problèmes peuvent être surmontés par un mélange fini de modèles linéaires mixtes (FMLME), qui segmente la population en sous-populations et modélise chacune de ces dernières par un LME distinct. Souvent, un grand nombre de variables explicatives sont introduites dans la phase initiale d'une étude. Cependant, leurs associations à la variable réponse varient d'un composant à l'autre du modèle FMLME. Afin d'améliorer la prévisibilité et de recueillir un modèle parcimonieux, il est d'un grand intérêt pratique d'identifier les effets importants, tant fixes qu'aléatoires, dans le modèle. Les techniques conventionnelles de sélection de variables telles que la suppression progressive et la sélection de sous-ensembles sont informatiquement chères, même lorsque le nombre de composants et de covariables est relativement modeste. La présente thèse introduit une approche basée sur la vraisemblance pénalisée et propose un algorithme EM imbriqué qui est computationnellement efficace. On démontre aussi que les estimateurs possèdent des propriétés telles que la cohérence, la parcimonie et la normalité asymptotique. On illustre la performance de la méthode proposée au moyen de simulations et d'une application sur un vrai jeu de données.
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Langan, Dean. "Estimating the heterogeneity variance in a random-effects meta-analysis." Thesis, University of York, 2015. http://etheses.whiterose.ac.uk/13507/.

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In a meta-analysis, differences in the design and conduct of studies may cause variation in effects beyond what is expected from chance alone. This additional variation is commonly known as heterogeneity, which is incorporated into a random-effects model. The heterogeneity variance parameter in this model is commonly estimated by the DerSimonian-Laird method, despite being shown to produce negatively biased estimates in simulated data. Many other methods have been proposed, but there has been less research into their properties. This thesis compares all methods to estimate the heterogeneity variance in both empirical and simulated meta-analysis data. First, methods are compared in 12,894 empirical meta-analyses from the Cochrane Database of Systematic Reviews (CDSR). These results showed high discordance in estimates of the heterogeneity variance between methods, so investigating their properties in simulated meta-analysis data is worthwhile. A systematic review of relevant simulation studies was then conducted and identified 12 studies, but there was little consensus between them and conclusions could only be considered tentative. A new simulation study was conducted in collaboration with other statisticians. Results confirmed that the DerSimonian-Laird method is negatively biased in scenarios where within-study variances are imprecise and/or biased. On the basis of these results, the REML approach to heterogeneity variance estimation is recommended. A secondary analysis combines simulated and empirical meta-analysis data and shows all methods usually have poor properties in practice; only marginal improvements are possible using REML. In conclusion, caution is advised when interpreting estimates of the heterogeneity variance and confidence intervals should always be presented to express its uncertainty. More promisingly, the Hartung-Knapp confidence interval method is robust to poor heterogeneity variance estimates, so sensitivity analysis is not usually required for inference on the mean effect.
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Yi, Qilong. "Random effects and AR(1) models in longitudinal data analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ49731.pdf.

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Häggström, Lundevaller Erling. "Tests of random effects in linear and non-linear models /." Umeå : Department of Statistics, University of Umeå, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-15.

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Jia, Yue. "Using sampling weights in the estimation of random effects model." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3258527.

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Thesis (Ph.D. in Statistical Science)--S.M.U., 2007.
Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 68-04, Section: B, page: 2431. Adviser: Lynne Stokes. Includes bibliographical references.
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Chen, Xiangyin. "Effects on Analysis Arising from Confidentialising Data Using Random Rounding." Thesis, University of Canterbury. Mathematics and Statistics, 2009. http://hdl.handle.net/10092/3756.

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Government statistical agencies collect data on individuals. These data can have personal information that will lead to individual identification. The information gathered is often released and used by other agencies. In order to preserve confidentiality (people’s privacy) the data are treated in a way that prevents identification. In recent years there has been a rapid increase in the research in the area of confidentiality and statistical disclosure techniques (SDC). We focus on random rounding method, one of the SDC. In this thesis we use rounded data which have been collected by Statistics NZ. We examine the effect of random rounding in contingency tables. We simulate data, based on rounded data, and actual data and use the general log-linear model and chi-square test for analysis.
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Dishman, Tamarah Crouse. "Identifying Outliers in a Random Effects Model For Longitudinal Data." UNF Digital Commons, 1989. http://digitalcommons.unf.edu/etd/191.

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Identifying non-tracking individuals in a population of longitudinal data has many applications as well as complications. The analysis of longitudinal data is a special study in itself. There are several accepted methods, of those we chose a two-stage random effects model coupled with the Estimation Maximization Algorithm (E-M Algorithm) . Our project consisted of first estimating population parameters using the previously mentioned methods. The Mahalanobis distance was then used to sequentially identify and eliminate non-trackers from the population. Computer simulations were run in order to measure the algorithm's effectiveness. Our results show that the average specificity for the repetitions for each simulation remained at the 99% level. The sensitivity was best when only a single non-tracker was present with a very different parameter a. The sensitivity of the program decreased when more than one tracker was present, indicating our method of identifying a non-tracker is not effective when the estimates of the population parameters are contaminated.
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Adams, Jesse Daniel Jackson John D. "Investigating health determinants in OECD countries a random effects analysis /." Auburn, Ala, 2008. http://hdl.handle.net/10415/1444.

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Ogden, Joshua Lee. "Modeling Random Dopant Fluctuation Effects in Nanoscale Tri-gate FETs." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/759.

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The tri-gate FET has been hailed as the biggest breakthrough in transistor technology in the last 20 years. The increase in device performance (faster switching, less delay, improved short channel effects, etc.) coupled with the reduction in device size, would allow for huge gains in the electronics industry. This thesis aims to not only investigate the validity of these claims, but also how random dopant fluctuation (RDF) affects the tri-gates performance and how to curb these issues. In order to achieve this, an atomistic 3-D device simulation program was utilized in order to capture the many quantum mechanical effects that devices of this size experience and compare the results against a similar planar device. We see the tri-gate FET does indeed perform extremely well compared to its planar counterpart, but both devices experience a great deal of fluctuations due to the random dopants in the device. In order to limit the RDF effects a variety of methods were implemented including increasing doping concentrations in the channel, source, and drain regions, varying the source/drain junction depths, and varying the source/drain contact workfunction. The results showed that increasing doping concentrations in order to reduce the amount of space the dopants had to diffuse did not reduce the randomness experienced by the devices, but rather the randomness increased. The dopant fluctuation was insensitive to the varying of the workfunction, but was found to decrease with an increase in junction depth in the source/drain regions. With randomness in the tri-gate reduced, the overall performance should increase when used in ICs, where consistency in device characteristics is essential.
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Imai, Takumi. "Exploratory assessment of treatment-dependent random-effects distribution using gradient functions." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/264638.

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京都大学
新制・論文博士
博士(社会健康医学)
乙第13422号
論社医博第16号
新制||社医||11(附属図書館)
京都大学大学院医学研究科社会健康医学系専攻
(主査)教授 佐藤 俊哉, 教授 藤渕 航, 教授 黒田 知宏
学位規則第4条第2項該当
Doctor of Public Health
Kyoto University
DFAM
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Prevost, Andrew Toby. "Multilevel modelling of child mortality : Gibbs sampling versus other approaches." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242478.

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Wolfe, Rory St John. "Models and estimation for repeated ordinal responses, with application to telecommunications experiments." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242240.

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34

Kensler, Jennifer Lin Karam. "Analysis of Reliability Experiments with Random Blocks and Subsampling." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28415.

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Reliability experiments provide important information regarding the life of a product, including how various factors may affect product life. Current analyses of reliability data usually assume a completely randomized design. However, reliability experiments frequently contain subsampling which is a restriction on randomization. A typical experiment involves applying treatments to test stands, with several items placed on each test stand. In addition, raw materials used in experiments are often produced in batches. In some cases one batch may not be large enough to provide materials for the entire experiment and more than one batch must be used. These batches lead to a design involving blocks. This dissertation proposes two methods for analyzing reliability experiments with random blocks and subsampling. The first method is a two-stage method which can be implemented in software used by most practitioners, but has some limitations. Therefore, a more rigorous nonlinear mixed model method is proposed.
Ph. D.
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35

Biard, Lucie. "Test des effets centre en épidémiologie clinique." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCC302.

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La modélisation des effets centre dans le cadre des données de survie repose souvent sur l'utilisation de modèles de Cox à effets mixtes. Tester un effet centre revient alors à tester à zéro la variance de l'effet aléatoire correspondant. La distribution sous l'hypothèse nulle des statistiques des tests paramétriques usuels n'est alors pas toujours connue. Les procédures de permutation ont été proposées comme alternative, pour les modèles linéaires généralisés mixtes.L'objectif est de développer, pour l'analyse des effets centre dans un modèle de survie de Cox à effets mixtes, une procédure de test de permutation pour les effets aléatoires.La première partie du travail présente la procédure de permutation développée pour le test d'un unique effet centre sur le risque de base, avec une application à la recherche d'un effet centre dans un essai clinique chez des patients atteints de leucémie myéloïde aiguë. La seconde partie porte sur l'extension de la procédure au test d'effets aléatoires multiples afin d’étudier à la fois des effets centre sur le risque de base et sur l'effet de variables, avec des illustrations sur deux cohortes de patients atteints de leucémie aiguë. Dans une troisième partie, les méthodes proposées sont appliquées à une cohorte multicentrique de patients en réanimation atteints d'hémopathies malignes, pour étudier les facteurs déterminant les effets centre sur la mortalité hospitalière. Les procédures de permutation proposées constituent une approche robuste et d'implémentation relativement aisée pour le test, en routine, d'effets aléatoires, donc un outil adapté pour l'analyse d'effets centre en épidémiologie clinique, afin de comprendre leur origine
Centre effects modelling within the framework of survival data often relies on the estimation of Cox mixed effects models. Testing for a centre effect consists in testing to zero the variance component of the corresponding random effect. In this framework, the identification of the null distribution of usual tests statistics is not always straightforward. Permutation procedures have been proposed as an alternative, for generalised linear mixed models.The objective was to develop a permutation test procedure for random effects in a Cox mixed effects model, for the test of centre effects.We first developed and evaluated permutation procedures for the test of a single centre effect on the baseline risk. The test was used to investigate a centre effect in a clinical trial of induction chemotherapy for patients with acute myeloid leukaemia.The second part consisted in extending the procedure for the test of multiple random effects, in survival models. The aim was to be able to examine both center effects on the baseline risk and centre effects on the effect of covariates. The procedure was illustrated on two cohorts of acute leukaemia patients. In a third part, the permutation approach was applied to a cohort of critically ill patients with hematologic malignancies, to investigate centre effects on the hospital mortality.The proposed permutation procedures appear to be robust approaches, easily implemented for the test of random centre effect in routine practice. They are an appropriate tool for the analysis of centre effects in clinical epidemiology, with the purpose of understanding their sources
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36

Atenafu, Eshetu Getachew. "Sequential tests for monitoring parameters of a nested random effects model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ59775.pdf.

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37

Ketchum, Jessica McKinney. "A Normal-Mixture Model with Random-Effects for RR-Interval Data." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1979.

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Ou, Zhaoyang. "An association model for specific-interaction effects in random copolymer solutions." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/9140.

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39

Lee, Katherine Jane. "Random effects models to allow for clustering in individually randomised trials." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431515.

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40

Fotouhi, A. R. "Longitudinal data analysis : the initial conditions problem in random effects modelling." Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387647.

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41

Boonsalee, Siwaphong 1974. "Effects of random surface errors on the performance of paraboloidal reflectors." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8940.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (leaves 146-151).
A program based on ray tracing has been developed to study the radiation patterns of paraboloidal reflector antennas whose surfaces are subjected to random errors with the emphasis on using an accurate representation of the statistics of the random surface errors. An ensemble of Gaussian random surfaces is created to be used with the Monte Carlo simulation. The average patterns from different surface root-mean-square values are presented for both the co-polarized and cross-polarized fields on the E-plane, H-plane, and 45-degree plane. They are compared with results based on physical optics and the antenna tolerance theory.
by Siwaphong Boonsalee.
M.Eng.and S.B.
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42

Ngaruye, Innocent. "Contributions to Small Area Estimation : Using Random Effects Growth Curve Model." Doctoral thesis, Linköpings universitet, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-137206.

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This dissertation considers Small Area Estimation with a main focus on estimation and prediction for repeated measures data. The demand of small area statistics is for both cross-sectional and repeated measures data. For instance, small area estimates for repeated measures data may be useful for public policy makers for different purposes such as funds allocation, new educational or health programs, etc, where decision makers might be interested in the trend of estimates for a specic characteristic of interest for a given category of the target population as a basis of their planning. It has been shown that the multivariate approach for model-based methods in small area estimation may achieve substantial improvement over the usual univariate approach. In this work, we consider repeated surveys taken on the same subjects at different time points. The population from which a sample has been drawn is partitioned into several non-overlapping subpopulations and within all subpopulations there is the same number of group units. The aim is to propose a model that borrows strength across small areas and over time with a particular interest of growth profiles over time. The model accounts for repeated surveys, group individuals and random effects variations. Firstly, a multivariate linear model for repeated measures data is formulated under small area estimation settings. The estimation of model parameters is discussed within a likelihood based approach, the prediction of random effects and the prediction of small area means across timepoints, per group units and for all time points are obtained. In particular, as an application of the proposed model, an empirical study is conducted to produce district level estimates of beans in Rwanda during agricultural seasons 2014 which comprise two varieties, bush beans and climbing beans. Secondly, the thesis develops the properties of the proposed estimators and discusses the computation of their first and second moments. Through a method based on parametric bootstrap, these moments are used to estimate the mean-squared errors for the predicted small area means. Finally, a particular case of incomplete multivariate repeated measures data that follow a monotonic sample pattern for small area estimation is studied. By using a conditional likelihood based approach, the estimators of model parameters are derived. The prediction of random effects and predicted small area means are also produced.
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43

HE, Ran. "Carry-over and interaction effects of different hand-milking techniques and milkers on milk." Thesis, Uppsala universitet, Statistiska institutionen, 1986. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154641.

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The main idea of this thesis is studying the importance of the carry-over effects and interaction effects in statistical models. To investigate it, a hand-milking experiment in Burkina Faso was studied. In many no electricity access countries, such as Burkina Faso, the amount of milk and milk compositions are still highly  relying on hand-milking techniques and milkers. Moreover, the time effects also plays a important role in stockbreeding system. Therefore, falling all effects, carry-over effects and interaction effects into a linear mixed effects model, it is concluded that the carry-over effects of milker and hand-milking techniques cannot be neglected, and the interaction effects among hand-milking techniques, different milkers, days and periods can be substantial.
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44

Boedeker, Peter. "Comparison of Heterogeneity and Heterogeneity Interval Estimators in Random-Effects Meta-Analysis." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157553/.

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Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Accurate estimation of heterogeneity is necessary as a description of the distribution and for determining weights applied in the estimation of the summary effect when using inverse-variance weighting. To evaluate a wide range of estimators, we compared 16 estimators (Bayesian and non-Bayesian) of heterogeneity with regard to bias and mean square error over conditions based on reviews of educational and psychological meta-analyses. Three simulation conditions were varied: (a) sample size per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Confidence or highest density intervals can be calculated for heterogeneity. The heterogeneity estimators that performed best over the widest range of conditions were paired with heterogeneity interval estimators. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. The combination of the Paule Manel estimator and Q-Profile interval method is recommended when synthesizing standardized mean difference effect sizes.
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Gu, Xiaoxiong. "Modeling effects of random rough surface on conductor loss at microwave frequencies /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/5831.

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Salabasis, Mickael. "Bayesian time series and panel models : unit roots, dynamics and random effects." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (Ekonomiska forskningsinstitutet vid Handelshögsk.) (EFI), 2004. http://www.hhs.se/efi/summary/632.htm.

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47

Hendrick, Angus Greer. "Effects of domain size on transverse permeability through random arrays of cylinders." Thesis, The University of Arizona, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3592730.

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Researchers using Darcy's law to model flow in porous media must satisfy the requirement for sufficient scale separation between the pore scale and the model scale. This requirement is analogous to that for any continuum model, where application is restricted to scales larger than the underlying discrete structure. In the case of Darcy's law when the model scale becomes too small, the measurement of the permeability—the material property required to close the relationship—becomes polluted by the boundary conditions, either physical or numerical. The requirements for adequate scale separation to obtain permeability measurements (also known as satisfying the conditions for a representative elementary volume, or REV, for permeability) have not been previously reported. Likewise, the behavior of Darcy models when applied at sub-REV length scales has not been reported.

Here, the results of Stokes simulations of transverse flow in 90,000 sequential random packings of monodisperse cylinders at a variety of liquid fractions and averaging-volume sizes show that approximately 200 cylinders must be present in an averaging volume before the effects of periodic boundary conditions on the Stokes simulations (the conventional choice for permeability measurements using Stokes flow) are no longer evident in the measured permeability. Direct comparisons between flow predictions from a two-dimensional, tensor-based Darcy model and a Stokes model for additional 10,000 domains show that the Darcy model is an unbiased predictor of the flow distribution in the system, even when the permeability is expected to contain boundary-condition artifacts. Though unbiased, the Darcy models do show considerable reduction in accuracy as the model scale shrinks toward the pore scale, with significant declines observed after the side length of a square averaging volume reaches 10 times the cylinder diameter. Finally, a novel approach for visualizing flows using the linear properties of the Stokes equations shows how the periodic boundary conditions affect the flow, and motivates the development of a generalized approach for obtaining permeability that does not require periodic boundary conditions. Modest improvements in the Darcy model relative to the actual Stokes flow result when the new approach is used to obtain permeability at small averaging volumes.

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Kazemi, Iraj. "The initial conditions problem in dynamic panel data models with random effects." Thesis, Lancaster University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431463.

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49

Vyas, Prerit. "Effects of Stochastic (Random) Surface Roughness on Hydrodynamic Lubrication of Deterministic Asperity." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_theses/344.

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In order to achieve enhanced and cost-effective performance of engineering components, Surface Engineering embraces traditional and innovative surface technologies which modify the surface properties of metallic and non-metallic engineering components for specific and sometime unique engineering purposes. The surface roughness of an engineered surface may be classified as: the random surface roughness which is a product of surface finishing and the deterministic surface roughness which is engineered to increase the lubrication characteristics of the hydro dynamically lubricated thrust ring. The effect of stochastic/random roughness can not be ignored when the roughness is of the same amplitude as that of fluid film thickness. Average flow model derived in terms of flow factors which are functions of the roughness characteristics is used to study the random surface roughness effects on hydrodynamic lubrication of deterministic asperity. In addition, the effect of boundary conditions on flow factors is studied by calculating the pressure and shear flow factor using two different new boundary conditions. The results are obtained for random surface roughness having a Gaussian distribution of roughness heights.
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Almohaimeed, Amani Mohammed. "Box-Cox-type transformations for linear and logistic models with random effects." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12831/.

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Random effect models have become a mainstream statistical technique over the last decades; and the same can be said for response transformation models such as the Box-Cox transformation. The latter ensures that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for the use of a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. The first aim of this thesis is to develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the “Nonparametric Maximum Likelihood” towards a “Nonparametric Profile Maximum Likelihood” (NPPML) technique. The implemented techniques allow to deal with overdispersion as well as two-level data scenarios in general linear models. The second part of this thesis considers the transformation of mixed-effects logistic models, with the aim of improving model fit. In binary data, link functions other than the logit can be used to connect predictors with the response. The Box-Cox transformation is used in mixed–effects binary regression models as an alternative link function for linearization purposes. The NPPML approach is used similarly as before, with some adjustments. The proposed approach is implemented in the R package boxcoxmix. Simulation studies and applications on real data are carried out to study the performance of this approach.
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